nerdexam
Microsoft

DP-100 · Question #198

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might h

The correct answer is B. No. The answer is No because the proposed solution uses an explainer that does not support both global and local feature importance. For example, PFIExplainer (Permutation Feature Importance) only produces global importance scores across the entire dataset and cannot explain individu

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Question

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You train a classification model by using a logistic regression algorithm. You must be able to explain the model's predictions by calculating the importance of each feature, both as an overall global relative importance value and as a measure of local importance for a specific set of predictions. You need to create an explainer that you can use to retrieve the required global and local feature importance values. Solution: Create a MimicExplainer. Does the solution meet the goal?

Options

  • AYes
  • BNo

How the community answered

(43 responses)
  • A
    21% (9)
  • B
    79% (34)

Explanation

The answer is No because the proposed solution uses an explainer that does not support both global and local feature importance. For example, PFIExplainer (Permutation Feature Importance) only produces global importance scores across the entire dataset and cannot explain individual predictions locally. To satisfy both requirements - overall global relative importance and local importance for a specific set of predictions - you must use an explainer such as TabularExplainer or MimicExplainer, which support both global and local explanations for logistic regression models.

Topics

#Model Interpretability#Explainable AI#Feature Importance#Azure Machine Learning Explainers

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